python对NOAA天气数据格式转换

转换NOAA天气数据文件“ .fly”为Pandas DataFrame

 获取数据 ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily

In [1]:

import  matplotlib.pyplot  as  plt 
import  pandas  as  pd 
import  numpy  as  np 
import  re 
import  ftplib

% matplotlib notebook

In [2]:

# download data from FTP

def  download_file_from_ftp ( FTP_SERVER , FTP_PATH , FILENAME ): 
    with  ftplib . FTP ( FTP_SERVER )  as  ftp : 
        ftp . login () 
        ftp . cwd ( FTP_PATH ) 
        with  open ( FILENAME ,  'wb' )  as  f : 
            ftp . retrbinary ( 'RETR '  +  FILENAME ,  f . write )

查询站ID

In [3]:

def  get_station_ID ( station_to_find ,  filename ): 
    for  line  in  open ( filename ): 
        if  station_to_find  in  line : 
            line_with_station = line 
            station_ID = re . split ( " " , line_with_station )[ 0 ] 
            return  station_ID 
    return  None 
# warning, it is slow, download it only once 
download_file_from_ftp ( "ftp.ncdc.noaa.gov" , "/pub/data/ghcn/daily" ,  "ghcnd-stations.txt" )

station_to_find = "GUANGZHOU"  # USE CAPS 
station_ID = get_station_ID ( station_to_find ,  "ghcnd-stations.txt" )

下载天气数据

In [4]:

weather_data_filename = station_ID + '.dly'

# warning, it is slow, download it only once 
download_file_from_ftp ( "ftp.ncdc.noaa.gov" ,  "/pub/data/ghcn/daily/all" ,  weather_data_filename )

将.fly转换为pandas Dataframe

In [7]:

 

df = convert_dly_to_dataframe ( weather_data_filename ) 
df . head ()

Out[7]:

	YEAR	MONTH	ELEMENT	VALUE1	VALUE2	VALUE3	VALUE4	VALUE5	VALUE6	VALUE7	…	VALUE22	VALUE23	VALUE24	VALUE25	VALUE26	VALUE27	VALUE28	VALUE29	VALUE30	VALUE31
0	1945	11	TAVG	NaN	NaN	NaN	NaN	NaN	NaN	NaN	…	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	107.0	NaN
1	1945	12	TAVG	123.0	136.0	152.0	144.0	146.0	189.0	219.0	…	179.0	146.0	128.0	107.0	104.0	112.0	122.0	127.0	129.0	156.0
2	1946	1	TAVG	150.0	150.0	123.0	117.0	112.0	121.0	125.0	…	146.0	153.0	173.0	196.0	211.0	212.0	218.0	201.0	156.0	131.0
3	1946	2	TAVG	114.0	112.0	147.0	181.0	195.0	192.0	149.0	…	201.0	196.0	231.0	226.0	221.0	229.0	240.0	NaN	NaN	NaN
4	1946	3	TAVG	237.0	162.0	142.0	133.0	183.0	187.0	160.0	…	183.0	192.0	205.0	216.0	223.0	238.0	207.0	195.0	233.0	228.0